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This study investigated whether radiomic features extracted from pretreatment [18F]FDG PET could improve the prediction of both histopathologic tumor response and survival in patients with locally advanced cervical cancer (LACC) treated with neoadjuvant chemoradiotherapy followed by surgery compared with conventional PET parameters and histopathologic features. Methods: The medical records of all consecutive patients with LACC referred between July 2010 and July 2016 were reviewed. [18F]FDG PET/CT was performed before neoadjuvant chemoradiotherapy. Radiomic features were extracted from the primary tumor volumes delineated semiautomatically on the PET images and reduced by factor analysis. A receiver-operating-characteristic analysis was performed, and conventional and radiomic features were dichotomized with Liu's method according to pathologic response (pR) and cancer-specific death. According to the study protocol, only areas under the curve of more than 0.70 were selected for further analysis, including logistic regression analysis for response prediction and Cox regression analysis for survival prediction. Results: A total of 195 patients fulfilled the inclusion criteria. At pathologic evaluation after surgery, 131 patients (67.2%) had no or microscopic (≤3 mm) residual tumor (pR0 or pR1, respectively); 64 patients (32.8%) had macroscopic residual tumor (>3 mm, pR2). With a median follow-up of 76.0 mo (95% CI, 70.7-78.7 mo), 31.3% of patients had recurrence or progression and 20.0% died of the disease. Among conventional PET parameters, SUVmean significantly differed between pathologic responders and nonresponders. Among radiomic features, 1 shape and 3 textural features significantly differed between pathologic responders and nonresponders. Three radiomic features significantly differed between presence and absence of recurrence or progression and between presence and absence of cancer-specific death. Areas under the curve were less than 0.70 for all parameters; thus, univariate and multivariate regression analyses were not performed. Conclusion: In a large series of patients with LACC treated with neoadjuvant chemoradiotherapy followed by surgery, PET radiomic features could not predict histopathologic tumor response and survival. It is crucial to further explore the biologic mechanism underlying imaging-derived parameters and plan a large, prospective, multicenter study with standardized protocols for all phases of the process of radiomic analysis to validate radiomics before its use in clinical routine.
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BACKGROUND: Positron emission tomography (PET) may be useful for defining the gross tumour volume for radiation treatment planning and for response monitoring of non-small cell lung cancer (NSCLC) patients. The purpose of this study was to compare tumour sizes obtained from CT- and various more commonly available PET-based tumour delineation methods to pathology findings. METHODS: Retrospective non-respiratory gated whole body [18F]-fluoro-2-deoxy-D-glucose PET/CT studies from 19 NSCLC patients were used. Several (semi-)automatic PET-based tumour delineation methods and manual CT-based delineation were used to assess the maximum tumour diameter. RESULTS: 50%, adaptive 41% threshold-based and contrast-oriented delineation methods showed good agreement with pathology after removing two outliers (R2=0.82). An absolute SUV threshold of 2.5 also showed a good agreement with pathology after the removal of 5 outliers (R2: 0.79), but showed a significant overestimation in the maximum diameter (19.8 mm, p<0.05). Adaptive 50%, relative threshold level and gradient-based methods did not show any outliers, provided only small, non-significant differences in maximum tumour diameter (<4.7 mm, p>0.10), and showed fair correlation (R2>0.62) with pathology. Although adaptive 70% threshold-based methods showed underestimation compared to pathology (36%), it provided the best precision (SD: 14%) together with good correlation (R2=0.81). Good correlation between CT delineation and pathology was observed (R2=0.77). However, CT delineation showed a significant overestimation compared with pathology (3.8 mm, p<0.05). CONCLUSIONS: PET-based tumour delineation methods provided tumour sizes in agreement with pathology and may therefore be useful to define the (metabolically most) active part of the tumour for radiotherapy and response monitoring purposes.
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BACKGROUND: [18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET) is a valuable tool for monitoring response to therapy in oncology. In longitudinal studies, however, patients are not scanned in exactly the same position. Rigid and non-rigid image registration can be applied in order to reuse baseline volumes of interest (VOI) on consecutive studies of the same patient. The purpose of this study was to investigate the impact of various image registration strategies on standardized uptake value (SUV) and metabolic volume test-retest variability (TRT). METHODS: Test-retest whole-body [18F]FDG PET/CT scans were collected retrospectively for 11 subjects with advanced gastrointestinal malignancies (colorectal carcinoma). Rigid and non-rigid image registration techniques with various degrees of locality were applied to PET, CT, and non-attenuation corrected PET (NAC) data. VOI were drawn independently on both test and retest scans. VOI drawn on test scans were projected onto retest scans and the overlap between projected VOI and manually drawn retest VOI was quantified using the Dice similarity coefficient (DSC). In addition, absolute (unsigned) differences in TRT of SUVmax, SUVmean, metabolic volume and total lesion glycolysis (TLG) were calculated in on one hand the test VOI and on the other hand the retest VOI and projected VOI. Reference values were obtained by delineating VOIs on both scans separately. RESULTS: Non-rigid PET registration showed the best performance (median DSC: 0.82, other methods: 0.71-0.81). Compared with the reference, none of the registration types showed significant absolute differences in TRT of SUVmax, SUVmean and TLG (p > 0.05). Only for absolute TRT of metabolic volume, significant lower values (p < 0.05) were observed for all registration strategies when compared to delineating VOIs separately, except for non-rigid PET registrations (p = 0.1). Non-rigid PET registration provided good volume TRT (7.7%) that was smaller than the reference (16%). CONCLUSION: In particular, non-rigid PET image registration showed good performance similar to delineating VOI on both scans separately, and with smaller TRT in metabolic volume estimates.
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BACKGROUND: Metabolic tumor volume assessment using positron-emission tomography [PET] may be of interest for both target volume definition in radiotherapy and monitoring response to therapy. It has been reported, however, that metabolic volumes derived from images of metabolic rate of glucose (generated using Patlak analysis) are smaller than those derived from standardized uptake value [SUV] images. The purpose of this study was to systematically compare metabolic tumor volume assessments derived from SUV and Patlak images using a variety of (semi-)automatic tumor delineation methods in order to identify methods that can be used reliably on (whole body) SUV images. METHODS: Dynamic [18F]-fluoro-2-deoxy-D-glucose [FDG] PET data from 10 lung and 8 gastrointestinal cancer patients were analyzed retrospectively. Metabolic tumor volumes were derived from both Patlak and SUV images using five different types of tumor delineation methods, based on various thresholds or on a gradient. RESULTS: In general, most tumor delineation methods provided more outliers when metabolic volumes were derived from SUV images rather than Patlak images. Only gradient-based methods showed more outliers for Patlak-based tumor delineation. Median measured metabolic volumes derived from SUV images were larger than those derived from Patlak images (up to 59% difference) when using a fixed percentage threshold method. Tumor volumes agreed reasonably well (< 26% difference) when applying methods that take local signal-to-background ratio [SBR] into account. CONCLUSION: Large differences may exist in metabolic volumes derived from static and dynamic FDG image data. These differences depend strongly on the delineation method used. Delineation methods that correct for local SBR provide the most consistent results between SUV and Patlak images.
